sagemaker-run-notebook
spotty
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| sagemaker-run-notebook | spotty | |
|---|---|---|
| 2 | 3 | |
| 139 | 491 | |
| -0.7% | 0.8% | |
| 0.0 | 0.0 | |
| 7 months ago | 6 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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sagemaker-run-notebook
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Experience migrating from Databricks to various AWS services
Notebooks are more of a tech/design debt. Job was originally created in notebooks and never refactored into a more maintainable solution. Do you have a recommendation on services to use with the papermill library? I saw the Sagemaker Convenience Package seems to rely on this a bit, but seems a little out of place for Sagemaker.
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Parameters for Jupyterlab GUI notebook scheduler using sagemaker-run-notebook
I am used to working on jupyter notebooks and have some experience with python scripting for extracting data. I created a notebook that extracts comments, runs sentiment analysis, and dumps data to S3- however, I am struggling with the CRON aspect of things. I want to automate running of the script every day at a set time. I am using the sagemaker-run-notebook package to do this, specifically using the GUI Jupyterlab extension. However, I am not sure what the parameters need to be. I tried a few combinations, and this is my best guess at the values. I am getting a JSON error, which I am not sure I understand. Can someone with experience working with on the sagemaker-run-notebook extension please help me out?
spotty
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[D] Interactive Compute Platform Recommendations for ML Research
Use spotty https://github.com/spotty-cloud/spotty to launch AWS Spot instances that you can use ssh port forwarding with to run GPU experiments from a jupyer notebook.
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[P]Spotml.io: Seamless ML training on AWS Spot instances, with docker (training 3X cheaper)
SpotML is a command line tool that automatically manages ML training on AWS spot instances. It lets you handle spot interruptions by resuming training using the latest checkpoint. Documentation link to try it out Looking for feedback from early testers. You would be an ideal candidate if you have a side project that you're spending your own money to train. Acknowledgement: - SpotML is built on top of existing open source library Spotty
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Show HN: Seamless ML training on AWS Spot instances
- SpotML is built on top of existing open source library Spotty: https://github.com/spotty-cloud/spotty
What are some alternatives?
elyra - Elyra extends JupyterLab with an AI centric approach.
ethereum-etl - Python scripts for ETL (extract, transform and load) jobs for Ethereum blocks, transactions, ERC20 / ERC721 tokens, transfers, receipts, logs, contracts, internal transactions. Data is available in Google BigQuery https://goo.gl/oY5BCQ
jupytext - Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
sagemaker-training-toolkit - Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
ipycanvas - Interactive Canvas in Jupyter
sagemaker-tensorflow-training-toolkit - Toolkit for running TensorFlow training scripts on SageMaker. Dockerfiles used for building SageMaker TensorFlow Containers are at https://github.com/aws/deep-learning-containers.
jupyterlab_templates - Support for jupyter notebook templates in jupyterlab
micro-service-email - Deploy a self-hosted gmail microservice in minutes
sagemaker-distribution - A set of Docker images that include popular frameworks for machine learning, data science and visualization.